Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension
論文解讀：使用圖注意力網絡進行多粒度機器閱讀理解的文檔建模 閱讀目的：學習該論文中基於文檔結構的多粒度建模。 摘要 「自然問題」是一種具有挑戰性的新機器閱讀理解基準，它具有兩個粒度的答案，即長答案（通常是一個段落）和短答案（長答案中有一個或多個實體）。儘管現有方法在此基準上很有效，但它們在訓練期間會分別對待這兩個子任務，而忽略了它們之間的依賴性。爲了解決這個問題，我們提出了一個新穎的多粒度機器閱讀
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相關文章
- 1. Attention-over-Attention Neural Networks for Reading Comprehension 訊飛
- 2. 論文《Adversarial Reading Networks For Machine Comprehension》
- 3. Bi-Directional Attention Flow For Machine Comprehension
- 4. Attention-over-Attention Neural Networks for Reading Comprehension論文筆記
- 5. 論文分享-->Attention-over-Attention Neural Networks for Reading Comprehension
- 6. 【一起讀ACL論文】Attention-over-Attention Neural Networks for Reading Comprehension
- 7. r-net:machine reading comprehension with self-matching networks
- 8. BI-DIRECTIONAL ATTENTION FLOW FOR MACHINE COMPREHENSION
- 9. Bidirectional Attention Flow for Machine Comprehension
- 10. 論文閱讀——Gated-Attention Readers for Machine Reading Comprehension